Quality Monitoring for Micro Resistance Spot Welding with Class-Imbalanced Data Based on Anomaly Detection

Abstract

Micro resistance spot welding (MRSW) is an important technology widely used in electronics manufacturing for micro component joining. For the joining of micro enameled wire, quality control is heavily dependent on manual inspection till now. In this paper, a quality monitoring approach based on isolation forest (iForest) is proposed to identify abnormal welds and normal welds. Electrode voltage and welding current of over 110,000 spot welds were collected from a production line. The dynamic resistance and heat input were calculated for all welds and used for feature extraction. A class imbalance problem existed in the collected dataset because abnormal welds were far fewer than normal welds. The anomaly detection model based on iForest was established for the imbalanced data classification after comparison with other methods such as one-class (support vector machine) SVM and local outlier factor. Test results show that the similarity of dynamic resistance profile and heat input compared with the previous ten welds are valid features for detecting a part of the abnormal welds. The iForest model is effective for distinguishing incomplete fusion welds from normal welds with high efficiency. It can assist in the on-line quality monitoring of enameled wire welding process in production.

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“…In comparison to conventional denoising techniques such as the Statistical Outlier Removal (SOR) filter and the Radius filter, the proposed method stands out with an impressive 30% enhancement in denoising accuracy, especially for noise points located near tree trunks. Zeng et al [27] focuses on the application of isolation forest (iForest) in quality monitoring for micro resistance spot welding (MRSW) used in electronics manufacturing, particularly for joining micro enameled wires. The iForest-based anomaly detection model effectively distinguishes incomplete fusion welds from normal ones and can enhance online quality monitoring in enameled wire welding processes in production.…”

“…Yet, machine learning algorithms frequently encounter challenges such as outliers and imbalanced datasets, which can reduce accuracy. Research has demonstrated that addressing these issues by applying the Isolation Forest (iForest) method to remove outliers [23][24][25][26][27] and utilizing Adaptive Synthetic Sampling (ADASYN) for balancing imbalanced data [28][29][30][31][32][33][34][35] can lead to improved predictive system performance.…”